Face-to-Face Interactions Estimated Using Mobile Phone Data to Support Contact Tracing Operations

Understanding people’s face-to-face interactions is crucial for effective infectious disease management. Traditional contact tracing, often relying on interviews or smartphone applications, faces limitations such as incomplete recall, low adoption rates, and privacy concerns. This study proposes utilizing anonymized Call Detail Records (CDRs) as a substitute for in-person meetings. We assume that when two individuals engage in a phone call connected to the same cell tower, they are likely to meet shortly thereafter. Testing this assumption, we evaluated two hypotheses. The first hypothesis—that such co-located interactions occur in a workplace setting—achieved 83% agreement, which is considered a strong indication of reliability. The second hypothesis—that calls made during these co-location events are shorter than usual—achieved 86% agreement, suggesting an almost perfect reliability level. These results demonstrate that CDR-based co-location events can serve as a reliable substitute for in-person interactions and thus hold significant potential for enhancing contact tracing and supporting public health efforts.

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Publication Details

Journal
Big Data and Cognitive Computing
Published
2024-12-30
DOI
https://doi.org/10.3390/bdcc9010004
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Face-to-Face Interactions Estimated Using Mobile Phone Data to Support Contact Tracing Operations

Győző Gidófalvi, Frederico Branco, Osvaldo Fernando Cossa, Nuno Sousa et al.
Big Data and Cognitive Computing
Human Mobility and Location-Based Analysis
article

Face-to-Face Interactions Estimated Using Mobile Phone Data to Support Contact Tracing Operations

Győző Gidófalvi, Frederico Branco, Osvaldo Fernando Cossa, Nuno Sousa, Silvino Pedro Cumbane
article en

Abstract

Understanding people’s face-to-face interactions is crucial for effective infectious disease management. Traditional contact tracing, often relying on interviews or smartphone applications, faces limitations such as incomplete recall, low adoption rates, and privacy concerns. This study proposes utilizing anonymized Call Detail Records (CDRs) as a substitute for in-person meetings. We assume that when two individuals engage in a phone call connected to the same cell tower, they are likely to meet shortly thereafter. Testing this assumption, we evaluated two hypotheses. The first hypothesis—that such co-located interactions occur in a workplace setting—achieved 83% agreement, which is considered a strong indication of reliability. The second hypothesis—that calls made during these co-location events are shorter than usual—achieved 86% agreement, suggesting an almost perfect reliability level. These results demonstrate that CDR-based co-location events can serve as a reliable substitute for in-person interactions and thus hold significant potential for enhancing contact tracing and supporting public health efforts.

Big Data and Cognitive ComputingVol. 9(1)
University of Trás-os-Montes and Alto Douro (PT), Eduardo Mondlane University (MZ), Universidade Aberta (PT), Institute for Systems Engineering and Computers (PT), INESC TEC (PT), KTH Royal Institute of Technology (SE)
Openalex Percentile: Top 78%
Human Mobility and Location-Based Analysis
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Face-to-Face Interactions Estimated Using Mobile Phone Data to Support Contact Tracing Operations — Győző Gidófalvi, Frederico Branco, et al. · Big Data and Cognitive Computing (2024) | TGRS Research Map | TGRS